Generally, people prefer graphics and visuals to pages of numeric values or text. Visual representations help address issues such as information overload or data glut. They allow people to more easily see the patterns and connections that are meaningful and important. If used appropriately, data visualization is a key component in the value-add
Chapter 5 ■ proCessing and adding VibranCy to sensor data
a primary catalyst in changing the manner in which sensor data is acted upon, either at an organizational level or by affecting the behaviors of individuals (for example, to initiate and maintain a sufficient level of physical activity). As the visualization process enables us to bring various information sources together, including non-sensor sources, context can be added that informs the interpretive process. Ultimately, visualization lets us create designs that tell a story about the data (McCandless, 2013). Good design, particularly in the health and wellness domains, often utilizes relative data, which connects the sensor data to peer data sets or to values that generate a fully rounded and qualified picture, rather than one based on absolutes, which could be misleading. The ability to visualize sensor data in a compelling manner allows individuals to evaluate the utility of a decision or to identify personal benefit to their health and wellbeing (as shown in Figure 5-3).
Figure 5-3. Mobile app visualization of a personal activity record on a temporal basis with supporting lifestyle targets The kinds of visualizations that are effective differ according to the context. For example, 3D visualizations typically require higher resolution, which may impose certain restrictions on their use in small form factor displays. It is therefore important to match the visualization wishes of the user—for example, differing views depending on specified context (the viewing device)—while addressing a meaningful real-world problem (Richter, 2009). There are seven potential classes of visualizations that can be used depending on the composition of the underlying dataset (Shneiderman, 1996), as shown in Figure 5-4.
Chapter 5 ■ proCessing and adding VibranCy to sensor data
1-, 2-, and 3-dimensionalrepresentations are common ways of organizing sensor data sets based on
combinations of characteristics, such as temporal or spatial components along with properties such as frequency, amplitude, and so forth. Visualization of multidimensional data sets may require modifying the dimensions in some manner, either reducing the number of dimensions for display or separating dimensions into different components for display. This approach is particularly advantageous when dealing with high-dimensional data sets that must be transposed into 2 or 3 dimensions for visualization (Rodrigues et al., 2010). Temporal representations are one of the most familiar methods of visualization because the data contains definite start and end times and can be represented by a timeline.
When the dimensionality of data can’t be reduced without loss of information, a multidimensional visualization approach is required. This often involves the creation of virtual sensors or other information sources to add context for sensor measurements. These forms of visualization are often customized for a particular application. For example, visualizations could fuse environmental sensor data with geospatial and cartography data in a hierarchical structure in the form of a tree. These are very useful for demonstrating the whole evolution of an aggregated value or virtual sensor reading or the relationships between groupings of similar sensors. Networks have similarities to trees in how
Chapter 5 ■ proCessing and adding VibranCy to sensor data
As sensor deployments grow in scale, the need to visualize distributed sensor networks, such as wireless sensor networks (WSNs), will increase. A number of efforts to address this requirement have been reported. HiperSense is designed to provide scalable sensor data visualization with the ability to handle up to 6200 independent stream of data (Chou et al., 2009). In the environmental sensing domain, Teris et al. discusses an environmental monitoring system based on MicaZ motes that was used to monitor soil temperature and moisture. The resulting data was visualized using Microsoft Research SensorMap, which enabled the geographic coordinates of the motes to be accessed via a web service interface. This allowed users to find specific sensor locations and to drill down into that location for both current and historical measurements, providing both a micro and macro view of the data with geographical context (Terzis et al., 2010).
Another popular tool for visualizing sensor data is Google’s Fusion tables. Fusion is a web-based application that allows users to gather, visualize, and share large data tables (Bradley et al., 2011, Fakoor et al., 2012). Once the data has been collected, the user can apply filters and create summaries across rows, up to hundreds of thousands. The user can visualize the data using charts, maps, or custom layouts and embed the visualization in a web page to share it with others. The tool enables citizens to upload a variety of environmental data sets, such as air and water quality data and contextual metrological data. It has also been used by researchers, such as those at the Waterbot program at the Create Lab in Carnegie Mellon University (CMU), to visualize data from water-quality sensors (temperature and conductivity sensing) deployed at the Nine Mile Run watershed in Pittsburgh, as shown in Figure 5-5. The researchers used Google Fusion tables to present their sensor data against a reference sensor (Solinst) with geographical sample mapping. They have been able to identify conductivity spikes that corresponded to sewage overflow events from heavy rainfall. In a blog post on the Climate Code, Professor Illah Nourbakhsh at CMU described Google Fusion tables as a key enabler of citizen science. He outlines how access to web-based data collection and visualization enables democratization of data, and in doing so empowers citizens to make informed decisions about their environment (Nourbakhsh, 2012).
Figure 5-5. Visualization of water quality sensor measurements using Google Fusion tables (with permission from the Create Lab, Carnegie Mellon University)
Chapter 5 ■ proCessing and adding VibranCy to sensor data
In the health and wellness domain, the ability to mine and visualize longitudinal data sets, such as behavior patterns at home, can provide early indications of abnormal behaviors and health-related issues (Lotfi et al., 2012). Visualization designs for this domain should accommodate both individual- and peer group-level norms as appropriate. Keep in mind that we are all individuals with our own unique biology, so adopted designs must be carefully selected in order to prevent “loaded” representations that yield narrow and restricted representations and do not reflect individual complexity.
Visualization can provide the tools for driving awareness in domains of interest. But, ultimately, its usefulness is determined by the ability to build accurate and relevant models that produce correct answers. Visualization is a powerful and effective tool for adding texture to sensor data sets and providing a mechanism to compress significant amounts of knowledge into an intuitive visual metaphor. However, visualization can only provide answers and insights to properly constructed questions. It is not a magic tool that can make sense from data chaos. It requires careful guidance to ensure data is correctly interpreted. In the future, data visualization will move beyond the current static modes of interactions to more interactive modalities, which will enable individuals to interact with data. Augmented reality solutions, such as Google Glass, will enable real-time delivery of visualized data that enhances our perception of the world and its impact on us. Ultimately, visualization is about adding vibrancy to sensor data so we can make connections to it and to tell meaningful stories about it.